Effect of increasing number of neurons using artificial neural network to estimate geoid heights

Nowadays the GPS measurements are one of the most frequently used technique in geodesy. With this technique ellipsoidal height can be reckoned. However in the engineering practice orthometric heights (height above sea level) are used. The orthometric heights are determined by levelling. Transforming the GPS-derived ellipsoidal heights to orthometric heights it is important to know the distance between the ellipsoidal and the geoid surface, called the geoid height or geoid undulation. GPS levelling method is easy to determine geoid height of related region. Geoid height calculated by soft computing methods such as fuzzy logic and neural networks has gained more popularity recently. In this study, it examined effect of increasing number of neurons in neural networks to determine geoid height. The neural network approach used in this study is based on a back propagation neural network learning the functional relationship between geographic position and geoid undulation. Thus, inputs to the neural network are geographic position (latitude and longitude), and the output from the network is the predicted geoid undulation.